Direct RNA targeted in situ sequencing for transcriptomic profiling in tissue

Highly multiplexed spatial mapping of transcripts within tissues allows for investigation of the transcriptomic and cellular diversity of mammalian organs previously unseen. Here we explore a direct RNA (dRNA) detection approach incorporating the use of padlock probes and rolling circle amplification in combination with hybridization-based in situ sequencing chemistry. We benchmark a High Sensitivity Library Preparation Kit from CARTANA that circumvents the reverse transcription needed for cDNA-based in situ sequencing (ISS) via direct RNA detection. We found a fivefold increase in transcript detection efficiency when compared to cDNA-based ISS and also validated its multiplexing capability by targeting a curated panel of 50 genes from previous publications on mouse brain sections, leading to additional data interpretation such as de novo cell clustering. With this increased efficiency, we also found to maintain specificity, multiplexing capabilities and ease of implementation. Overall, the dRNA chemistry shows significant improvements in target detection efficiency, closing the gap to other fluorescent in situ hybridization-based technologies and opens up possibilities to explore new biological questions previously not possible with cDNA-based ISS.

A total of 28 clusters were defined after the clustering the segmented data with the Leiden algorithm. Those clusters were annotated based on known cell type markers expressed in different clusters and the direct comparison between these clusters and cell types described by Zeisel et al. 1 to be present in the same brain region characterized in this experiment.
A total of 12 clusters were classified as excitatory neurons, characterized by the expression of markers such as Slc17a7 or Calb2. Excitatory neurons were mostly found along the cortex, the hypothalamus and, in less abundance, in the thalamus. Some distinct spatial and molecular diversity could be distinguished within excitatory populations due to the expression markers differentially expressed among different excitatory populations. An example of this diversity are clusters EXC4/5, which are found in Layer3/4 in the cortex and express Rorb.
Four different clusters were described to be capturing different inhibitory cell types. Two main groups could be described among the inhibitory clusters: 2 clusters which cells are mainly located in the cortex, and other 2 region-specific clusters, including clusters located in the caudoputamen (INH1) and one cluster in the reticular nucleus of the thalamus (INH2). These two clusters, despite their distinct location in the UMAP (Figure 2b) have been assigned to inhibitory clusters due to the resemblance between their expression profile and the inhibitory clusters' one, although they might need to be classified in a distinct group of cells.
We also described 9 clusters as non-neuronal clusters, since their expression profile presents specific markers for typical non-neuronal cell types. Among these, we found 5 oligodendrocyte-like clusters, characterized by the expression of markers like Plp1, Mbp or Sox10. Most of the cells assigned to these clusters are found in the fiber tracts. We also described one cluster showing an astrocyte-like expression profile, characterized by the expression of markers such as Gfap or Mgfe8. The population wasn't found in a specific spatial location. We also defined clusters assigned to endothelial cells (1) and ependymal cells and choroid plexus (1). Some other clusters didn't match well with any specific cell type described in Zeisel et al. 1 , but were characterized by the expression of certain genes, showing a clear location in the tissue. This is the case of Tac2+ cells, located in the medial habenula and Calb2+ cells, placed in specific regions of the thalamus. In the case of Aldoc+ cells, no specific location was found, but cells were characterized by a high expression of Aldoc. Finally, one cluster could not be annotated, since it presents a wide expression across the tissue and their expression profile didn't match any specific cell type, having levels of expression for both neuronal and non-neuronal markers. We believe this cluster could be an artifact due to a bad segmentation of certain cells across the tissue.

Supplementary Note 2: Comparison of clusters
In order to compare the expression of the clusters defined by dRNA-HybISS and osmFISH using the list of 33 genes in Codeluppi et al. 3 and the cell types defined using scRNA-seq data from Zeisel et al. 1 , both datasets were integrated using Spatial Gene Enrichment (Figure 3c). This integration shows high correspondence for most of the clusters found with the two spatial techniques, even though dRNA-HybISS presents a lower detection efficiency. One-to-one correspondence between clusters was observed for many of the of the non-neuronal cell types, including endothelial cells, perivascular macrophages, vascular smooth muscle cells, pericytes and ependymal cells. Regarding clusters expressing oligodendrocyte markers, we were able to clearly distinguish oligodendrocyte precursor cells (OPC), committed OPCs (Olig COP) and, to some extent newly formed Oligodendrocytes (Olig NF) and myelin forming oligodendrocytes (Olig MF) but further subtypes were not clearly distinguishable.
Within neuronal clusters, some discrepancies were present between the neuronal subtypes. The most important discrepancies between the methods were found among the excitatory cells where both methods showed poor capacities to resolve cell types clearly. In this case, no clear one-to-one correspondence can be found within excitatory clusters, except for Pyramidal L4 and Pyramidal L5, where a correlation between a specific osmFISH and a dRNA-HybISS cluster is observed.
Our clusters were also compared with the cell types described by Zeisel et al. 1 by scRNA-seq. As observed when comparing dRNA-HybISS clusters with the osmFISH ones, main cell classes could easily be assigned to each of the clusters detected by dRNA-HybISS (Supplementary Figure 9). Similar conclusions are extracted when comparing the osmFISH dataset with the scRNA-seq based cell types, where osmFISH clusters do not match perfectly the scRNA-seq ones for a considerable amount of excitatory and inhibitory populations.
The comparison of the clusters found by the three methods shows that, despite having lower detection efficiency, dRNA-HybISS is able to define cell types with a similar resolution level as osmFISH. Discrepancies between the clusters defined by scRNA-seq clustering and both spatial methods are consistent, proving the importance of the gene panel curation in targeted methods like these.  (3) Rolling circle amplification (4) Rolling circle product detection and imaging (5) Stripping and repeated cycles for combinatorial barcode decoding. b, Regional localization of mouse brain coronal section used for analysis. Image credit: Allen Brain Institute. c, Example of combinatorial decoding that is possible with dRNA-HybISS and gene panels used.    c, Dot plot representing the expression of different genes across excitatory cluster subtypes. Excitatoryspecific markers as well as three inhibitory markers (Vip, Gad2 and Sst).
Supplementary Figure 6 a b    b, Comparison between the osmFISH clusters, described in Codeluppi et al. 3 , and scRNA-seq clusters 1 when integrated using Spatial Gene Enrichment.